Person Re-identification Using Partial Least Squares Appearance Modeling

  • Gabriel Lorencetti Prado
  • William Robson Schwartz
  • Helio Pedrini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

Due to the large areas covered by surveillance systems, employed cameras usually lack intersection of field of view, refraining us from mapping the location of a person in a camera to another one. Therefore, when a subject appears in a camera, a person re-identification method is required to discover whether the subject has been previously identified in a different camera. Even though several approaches have been proposed in the literature, person re-identification is still a challenging problem due to appearance variation between cameras, changes in illumination, pose variation, and low quality data, among others. To reduce the effect of the aforementioned difficulties, we propose a person re-identification approach that models the appearance of the subjects based on multiple samples collected from multiple cameras and employs person detection and tracking to enhance the robustness of the method. Experiments conducted on three public available data sets demonstrate improvements over existing methods.

Keywords

person re-identification partial least squares appearance-based modeling person detection object tracking 

References

  1. 1.
    Hu, W., Hu, M., Zhou, X., Tan, T., Lou, J., Maybank, S.: Principal Axis-Based Correspondence between Multiple Cameras for People Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 663–671 (2006)CrossRefGoogle Scholar
  2. 2.
    Mazzon, R., Tahir, S.F., Cavallaro, A.: Person re-identification in crowd. Pattern Recognition Letters 33(14), 1828–1837 (2012)CrossRefGoogle Scholar
  3. 3.
    Satta, R.: Appearance Descriptors for Person Re-identification: a Comprehensive Review. CoRR abs/1307.5748 (2013)Google Scholar
  4. 4.
    Hirzer, M., Beleznai, C., Köstinger, M., Roth, P.M., Bischof, H.: Dense Appearance Modeling and Efficient Learning of Camera Transitions for Person Re-Identification. In: International Conference on Image Processing (2012)Google Scholar
  5. 5.
    Schwartz, W.R., Davis, L.S.: Learning Discriminative Appearance-Based Models Using Partial Least Squares. In: Brazilian Symposium on Computer Graphics and Image Processing (2009)Google Scholar
  6. 6.
    Schwartz, W.R.: Scalable People Re-Identification Based on a One-Against-Some Classification Scheme. In: International Conference on Image Processing (2012)Google Scholar
  7. 7.
    Gheissari, N., Sebastian, T.B., Hartley, R.: Person Reidentification Using Spatiotemporal Appearance. In: Computer Vision and Pattern Recognition (2006)Google Scholar
  8. 8.
    Hamdoun, O., Moutarde, F., Stanciulescu, B., Steux, B.: Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. In: International Conference on Distributed Smart Cameras (2008)Google Scholar
  9. 9.
    Kuo, C.-H., Huang, C., Nevatia, R.: Inter-camera association of multi-target tracks by on-line learned appearance affinity models. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 383–396. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Bazzani, L., Cristani, M., Perina, A., Farenzena, M., Murino, V.: Multiple-Shot Person Re-identification by HPE Signature. In: International Conference on Pattern Recognition (2010)Google Scholar
  11. 11.
    Bazzani, L., Cristani, M., Murino, V.: Symmetry-Driven Accumulation of Local Features for Human Characterization and Re-identification. Computer Vision and Image Understanding 117(2), 130–144 (2013)Google Scholar
  12. 12.
    Li, M., Chen, W., Huang, K., Tan, T.: Visual Tracking via Incremental Self-tuning Particle Filtering on the Affine Group. In: Computer Vision and Pattern Recognition (2010)Google Scholar
  13. 13.
    Cai, Y., Pietikäinen, M.: Person Re-identification Based on Global Color Context. In: Koch, R., Huang, F. (eds.) ACCV 2010 Workshops, Part I. LNCS, vol. 6468, pp. 205–215. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Alahi, A., Vandergheynst, P., Bierlaire, M., Kunt, M.: Cascade of Descriptors to Detect and Track Objects Across Any Network of Cameras. Computer Vision and Image Understanding 114(6), 624–640 (2010)CrossRefGoogle Scholar
  15. 15.
    Zheng, W.S., Gong, S., Xiang, T.: Transfer Re-identification: From Person to Set-based Verification. In: Computer Vision and Pattern Recognition (2012)Google Scholar
  16. 16.
    Wold, H.: Partial Least Squares. Encyclopedia of Statistical Sciences 6, 581–591 (1985)Google Scholar
  17. 17.
    International Conference on Pattern Recognition Contest - People tracking in wide baseline camera networks (2012), http://www.wide-baseline-camera-network-contest.org
  18. 18.
    Schwartz, W.R., Guo, H., Choi, J., Davis, L.S.: Face Identification Using Large Feature Sets. IEEE Transactions on Image Processing 21(4), 2245–2255 (2012)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Schwartz, W.R., Kembhavi, A., Harwood, D., Davis, L.S.: Human Detection Using Partial Least Squares Analysis. In: International Conference on Computer Vision (2009)Google Scholar
  20. 20.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. Technical report, Department of Computer Science, University of North Carolina, USA (1995)Google Scholar
  21. 21.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: Computer Vision and Pattern Recognition (2005)Google Scholar
  22. 22.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics SMC 3, 610–621 (1973)CrossRefGoogle Scholar
  23. 23.
  24. 24.
    Thirde, D., Li, L., Ferryman, J.: Overview of the PETS2006 Challenge. In: International Workshop on Performance Evaluation of Tracking and Surveillance (2006)Google Scholar
  25. 25.
    Yin, F., Makris, D., Velastin, S.A.: Performance Evaluation of Object Tracking Algorithms. In: International Workshop on Performance Evaluation of Tracking and Surveillance (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gabriel Lorencetti Prado
    • 1
  • William Robson Schwartz
    • 2
  • Helio Pedrini
    • 1
  1. 1.Institute of ComputingUniversity of CampinasCampinasBrazil
  2. 2.Department of Computer ScienceUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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